In [ ]:
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
In [ ]:
# Load data
P8_C_F_otus_data = pd.read_csv('./P8_C_F/data_normalized.csv',sep=',',index_col=0)
P8_C_F_metadata = pd.read_csv('./P8_C_F/P8_C_F.csv',sep=',',index_col=0)
P8_C_F_otus_data.head(10)
Out[Â ]:
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | ... | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 49 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Lactobacillus | 8.897736e+06 | 8.567618e+06 | 8.742705e+06 | 2.759672e+05 | 6.973386e+05 | 2.561120e+05 | 8.943864e+06 | 8.768577e+06 | 1.449831e+06 | 5.038006e+05 | ... | 5.294519e+06 | 7.169531e+06 | 6.686188e+06 | 5.306953e+06 | 3.559897e+06 | 7.753555e+05 | 5.392391e+06 | 5.250797e+06 | 8.060608e+06 | 7.510279e+06 |
| Escherichia_Shigella | 1.444014e+04 | 1.423959e+04 | 7.019514e+03 | 8.793847e+06 | 7.525722e+06 | 7.659895e+06 | 1.421953e+05 | 6.618399e+03 | 7.227492e+06 | 7.914201e+06 | ... | 4.294138e+06 | 1.897475e+06 | 2.988709e+06 | 4.332845e+06 | 5.436514e+06 | 8.336977e+06 | 4.162773e+06 | 4.361726e+06 | 5.597561e+05 | 1.748060e+06 |
| Muribacter | 1.642566e+05 | 1.349752e+05 | 1.772929e+05 | 8.022302e+03 | 2.843906e+05 | 8.343194e+04 | 1.464070e+05 | 2.761677e+05 | 4.432322e+04 | 2.202122e+05 | ... | 3.850705e+04 | 8.423417e+04 | 4.773270e+04 | 3.409478e+04 | 8.824532e+03 | 1.925352e+04 | 6.959347e+04 | 7.440685e+04 | 3.210926e+05 | 1.564349e+05 |
| Staphylococcus | 1.772929e+05 | 4.336054e+05 | 3.461623e+05 | 1.427970e+05 | 8.575841e+05 | 7.300295e+04 | 8.022302e+02 | 1.002788e+03 | 7.364473e+05 | 1.007200e+06 | ... | 2.607248e+03 | 3.610036e+03 | 4.011151e+03 | 2.807806e+03 | 2.787750e+04 | 9.827320e+03 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 |
| Streptococcus | 5.653717e+05 | 5.900403e+05 | 5.286697e+05 | 5.415054e+03 | 6.137061e+04 | 2.386635e+04 | 5.633662e+05 | 7.573053e+05 | 2.547081e+04 | 9.907543e+04 | ... | 2.264295e+05 | 2.859951e+05 | 1.768918e+05 | 2.097832e+05 | 1.929364e+05 | 7.681354e+04 | 2.360562e+05 | 2.200116e+05 | 8.792443e+05 | 4.207697e+05 |
| Enterococcus | 1.062955e+04 | 2.807806e+03 | 1.403903e+03 | 1.283568e+04 | 8.423417e+03 | 3.228977e+04 | 2.607248e+03 | 3.610036e+03 | 8.824532e+03 | 5.615611e+03 | ... | 2.607248e+03 | 4.011151e+03 | 5.415054e+03 | 1.584405e+04 | 1.042899e+04 | 4.612824e+03 | 1.604460e+03 | 2.206133e+03 | 1.403903e+03 | 3.208921e+03 |
| Pseudomonas | 0.000000e+00 | 2.005575e+02 | 2.005575e+02 | 0.000000e+00 | 0.000000e+00 | 3.208921e+03 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 1.604460e+03 | ... | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 2.005575e+02 | 4.011151e+02 | 1.203345e+03 | 0.000000e+00 | 8.022302e+02 | 2.005575e+02 | 0.000000e+00 |
| Lachnospiraceae_NK4A136_group | 4.612824e+03 | 1.343736e+04 | 1.403903e+04 | 3.971039e+04 | 3.088586e+04 | 1.079000e+05 | 8.824532e+03 | 1.183290e+04 | 2.767694e+04 | 1.383847e+04 | ... | 7.621187e+03 | 2.908084e+04 | 5.013939e+03 | 5.615611e+03 | 4.652935e+04 | 4.672991e+04 | 9.025090e+03 | 1.604460e+03 | 1.243457e+04 | 8.824532e+03 |
| Muribaculaceae | 3.228977e+04 | 5.856280e+04 | 3.329255e+04 | 1.961453e+05 | 1.379836e+05 | 4.362127e+05 | 4.793325e+04 | 4.211709e+04 | 1.472092e+05 | 7.139849e+04 | ... | 3.770482e+04 | 1.367802e+05 | 2.065743e+04 | 2.266300e+04 | 1.937386e+05 | 1.867191e+05 | 2.527025e+04 | 2.105854e+04 | 5.134273e+04 | 4.652935e+04 |
| Lachnospiraceae | 1.805018e+03 | 4.612824e+03 | 4.011151e+03 | 2.065743e+04 | 9.225647e+03 | 4.973827e+04 | 5.013939e+03 | 3.610036e+03 | 7.019514e+03 | 4.612824e+03 | ... | 2.206133e+03 | 1.083011e+04 | 1.805018e+03 | 2.005575e+03 | 1.805018e+04 | 2.446802e+04 | 2.206133e+03 | 1.805018e+03 | 3.008363e+03 | 1.805018e+03 |
10 rows × 48 columns
In [ ]:
def sort_meta(P8_C_F_metadata,condition = "Experiment"):
new_meta_index = []
for index1 in P8_C_F_metadata.index:
new_meta_index.append(str(index1))
P8_C_F_metadata.index = new_meta_index
metadata = P8_C_F_metadata.sort_values(condition)
return metadata
metadata = sort_meta(P8_C_F_metadata)
metadata.head(10)
Out[Â ]:
| Experiment | |
|---|---|
| 1 | C |
| 47 | C |
| 46 | C |
| 43 | C |
| 42 | C |
| 41 | C |
| 40 | C |
| 30 | C |
| 48 | C |
| 24 | C |
In [ ]:
def create_heatmap(otus_data,metadata,condition ="Experiment" ):
heatmap = otus_data
otus_data = otus_data.transpose()
new_column = []
new_idx = []
for index1 in otus_data.index:
new_idx.append(str(index1))
otus_data.index = new_idx
# print(otus_data)
for index1 in otus_data.index:
for index2 in metadata.index:
value = metadata.loc[index2, condition]
if str(index1) == str(index2):
new_column.append(value)
otus_data[condition] = new_column
otus_data = otus_data.sort_values(by=condition)
# print(otus_data)
heatmap = otus_data.drop(columns=[condition])
heatmap = heatmap.transpose()
return heatmap
heatmap = create_heatmap(P8_C_F_otus_data,P8_C_F_metadata)
heatmap.head(10)
Out[Â ]:
| 1 | 47 | 46 | 43 | 42 | 41 | 40 | 30 | 48 | 24 | ... | 26 | 9 | 10 | 21 | 20 | 19 | 18 | 17 | 34 | 32 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Lactobacillus | 8.897736e+06 | 5.250797e+06 | 5.392391e+06 | 5.306953e+06 | 6.686188e+06 | 7.169531e+06 | 5.294519e+06 | 6.901988e+06 | 8.060608e+06 | 6.695213e+06 | ... | 9.580634e+05 | 1.449831e+06 | 5.038006e+05 | 1.655402e+06 | 3.256052e+06 | 5.003911e+05 | 1.514410e+06 | 1.949419e+05 | 7.741521e+04 | 2.219169e+06 |
| Escherichia_Shigella | 1.444014e+04 | 4.361726e+06 | 4.162773e+06 | 4.332845e+06 | 2.988709e+06 | 1.897475e+06 | 4.294138e+06 | 2.847316e+06 | 5.597561e+05 | 2.905678e+06 | ... | 7.556006e+06 | 7.227492e+06 | 7.914201e+06 | 7.102345e+06 | 5.918052e+06 | 8.609133e+06 | 8.115962e+06 | 8.472754e+06 | 8.809490e+06 | 7.340206e+06 |
| Muribacter | 1.642566e+05 | 7.440685e+04 | 6.959347e+04 | 3.409478e+04 | 4.773270e+04 | 8.423417e+04 | 3.850705e+04 | 7.881912e+04 | 3.210926e+05 | 2.005575e+04 | ... | 4.011151e+02 | 4.432322e+04 | 2.202122e+05 | 3.870761e+04 | 7.821744e+03 | 3.389423e+04 | 1.484126e+04 | 1.103067e+04 | 2.647360e+04 | 1.444014e+04 |
| Staphylococcus | 1.772929e+05 | 0.000000e+00 | 0.000000e+00 | 2.807806e+03 | 4.011151e+03 | 3.610036e+03 | 2.607248e+03 | 0.000000e+00 | 0.000000e+00 | 7.019514e+03 | ... | 1.002788e+04 | 7.364473e+05 | 1.007200e+06 | 3.329255e+04 | 9.506428e+04 | 1.873208e+05 | 1.209362e+05 | 6.297507e+04 | 0.000000e+00 | 1.805018e+03 |
| Streptococcus | 5.653717e+05 | 2.200116e+05 | 2.360562e+05 | 2.097832e+05 | 1.768918e+05 | 2.859951e+05 | 2.264295e+05 | 1.399892e+05 | 8.792443e+05 | 2.454824e+05 | ... | 2.406691e+03 | 2.547081e+04 | 9.907543e+04 | 4.412266e+03 | 1.644572e+04 | 2.426746e+04 | 2.888029e+04 | 2.908084e+04 | 8.022302e+02 | 9.225647e+03 |
| Enterococcus | 1.062955e+04 | 2.206133e+03 | 1.604460e+03 | 1.584405e+04 | 5.415054e+03 | 4.011151e+03 | 2.607248e+03 | 0.000000e+00 | 1.403903e+03 | 1.002788e+03 | ... | 1.484126e+04 | 8.824532e+03 | 5.615611e+03 | 1.022844e+04 | 4.612824e+03 | 7.220072e+03 | 3.409478e+03 | 1.524237e+04 | 1.022844e+04 | 2.807806e+03 |
| Pseudomonas | 0.000000e+00 | 8.022302e+02 | 0.000000e+00 | 2.005575e+02 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 2.005575e+02 | 0.000000e+00 | ... | 3.610036e+03 | 0.000000e+00 | 1.604460e+03 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 1.203345e+03 | 0.000000e+00 | 4.011151e+02 |
| Lachnospiraceae_NK4A136_group | 4.612824e+03 | 1.604460e+03 | 9.025090e+03 | 5.615611e+03 | 5.013939e+03 | 2.908084e+04 | 7.621187e+03 | 3.008363e+03 | 1.243457e+04 | 6.417842e+03 | ... | 6.818957e+04 | 2.767694e+04 | 1.383847e+04 | 7.139849e+04 | 4.332043e+04 | 3.108642e+04 | 9.426205e+03 | 6.137061e+04 | 5.796113e+04 | 2.346523e+04 |
| Muribaculaceae | 3.228977e+04 | 2.105854e+04 | 2.527025e+04 | 2.266300e+04 | 2.065743e+04 | 1.367802e+05 | 3.770482e+04 | 3.409478e+03 | 5.134273e+04 | 3.008363e+04 | ... | 3.006358e+05 | 1.472092e+05 | 7.139849e+04 | 2.525020e+05 | 1.652594e+05 | 1.413931e+05 | 4.993883e+04 | 2.900062e+05 | 3.036441e+05 | 9.165480e+04 |
| Lachnospiraceae | 1.805018e+03 | 1.805018e+03 | 2.206133e+03 | 2.005575e+03 | 1.805018e+03 | 1.083011e+04 | 2.206133e+03 | 0.000000e+00 | 3.008363e+03 | 6.016726e+02 | ... | 3.489701e+04 | 7.019514e+03 | 4.612824e+03 | 4.131486e+04 | 1.805018e+04 | 2.125910e+04 | 2.206133e+03 | 3.349311e+04 | 3.028419e+04 | 7.621187e+03 |
10 rows × 48 columns
In [ ]:
def colordict(metadata,condition ='Experiment' ):
color_dict=dict(zip(np.unique(metadata[condition]),np.array(['g','blue'])))
row_colors = metadata[condition].map(color_dict)
return color_dict,row_colors
color_dict,row_colors = colordict(metadata)
In [ ]:
row_colors.head(10)
Out[Â ]:
1 g 47 g 46 g 43 g 42 g 41 g 40 g 30 g 48 g 24 g Name: Experiment, dtype: object
In [ ]:
color_dict
Out[Â ]:
{'C': 'g', 'F': 'blue'}
P8 Control vs Feeding (Genus)¶
In [ ]:
def plot_cluster_heatmap(heatmap,color_dict,row_colors,title):
custom_cmap = sns.color_palette("OrRd", as_cmap=True)
hm = sns.clustermap(heatmap,
metric="correlation",
standard_scale=0,
z_score=None,
col_colors=row_colors,
col_cluster=False,
cmap=custom_cmap,
# cbar_pos=(0, .2, .03, .4),
figsize=(14, 12))
# Create a color legend using the color_dict
legend_labels = [f"{experiment}" for experiment, color in color_dict.items()]
legend_colors = [color for _, color in color_dict.items()]
legend_handles = [plt.Line2D([0], [0], marker='o', color='w', label=label, markersize=10, markerfacecolor=color) for label, color in zip(legend_labels, legend_colors)]
plt.legend(handles=legend_handles, title="Experiment", bbox_to_anchor=(15, 1), loc='upper left')
# Add a title to the center of the heatmap
ax = hm.ax_heatmap
ax.text(0.5, 1.1, title, fontsize=12, ha="center", va="center", transform=ax.transAxes)
# Get the current Axes objects
ax_row_labels = hm.ax_row_dendrogram
ax_col_labels = hm.ax_col_dendrogram
# Set row and column labels font size
row_font_size = 4
col_font_size = 4
for label in ax_row_labels.get_yticklabels():
label.set_fontsize(row_font_size)
for label in ax_col_labels.get_xticklabels():
label.set_fontsize(col_font_size)
# Display the plot
plt.show()
title = "P8 Control vs Feeding (Genus)"
plot_cluster_heatmap(heatmap,color_dict,row_colors,title)
P11_NEC_Non-NEC_Colon¶
Load datasets¶
In [ ]:
# Load data
P11_C_NEC_otus_data = pd.read_csv('./P11_NEC_Non-NEC/Colon/tsv/data_normalized.csv',sep=',',index_col=0)
P11_C_NEC_metadata = pd.read_csv('./P11_NEC_Non-NEC/Colon/tsv/NEC_Colon.csv',sep=',',index_col=0)
P11_C_NEC_otus_data.head(10)
Out[Â ]:
| 050A | 051A | 052A | 053A | 054A | 055A | 056A | 057A | 058A | 059A | ... | 239A | 240A | 241A | 242A | 243A | 244A | 245A | 246A | 247A | 248A | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Lactobacillus | 6.057526e+06 | 6.305880e+06 | 7.359362e+06 | 2.354164e+06 | 2.067691e+06 | 1.836664e+06 | 2.043433e+06 | 8.016634e+05 | 5.478803e+06 | 5.900427e+06 | ... | 5.451080e+06 | 4.942821e+06 | 9.922606e+05 | 9.691579e+05 | 4.285549e+05 | 1.417350e+06 | 1.172462e+06 | 1.069655e+06 | 1.736167e+06 | 1.845905e+06 |
| Escherichia_Shigella | 3.487351e+06 | 3.416888e+06 | 2.185515e+06 | 5.077972e+06 | 4.606677e+06 | 6.169574e+06 | 5.613954e+06 | 6.446806e+06 | 4.056833e+06 | 3.863925e+06 | ... | 2.090794e+05 | 2.136999e+05 | 1.773132e+06 | 4.032575e+06 | 1.888645e+06 | 5.925840e+05 | 1.147049e+06 | 1.929075e+06 | 2.555158e+06 | 2.952524e+06 |
| Muribacter | 2.079242e+04 | 3.003350e+04 | 2.194756e+04 | 2.310269e+03 | 4.158484e+04 | 1.963729e+04 | 1.108929e+05 | 0.000000e+00 | 4.042971e+04 | 1.039621e+04 | ... | 2.495091e+05 | 1.686496e+05 | 4.505025e+04 | 0.000000e+00 | 0.000000e+00 | 8.085942e+03 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 |
| Staphylococcus | 0.000000e+00 | 3.465404e+03 | 0.000000e+00 | 1.686496e+06 | 1.957953e+06 | 8.085942e+05 | 1.058103e+06 | 1.145893e+06 | 4.620538e+03 | 2.310269e+03 | ... | 4.505025e+04 | 4.273998e+04 | 5.105695e+05 | 3.361442e+05 | 2.021486e+05 | 5.198106e+04 | 2.194756e+05 | 4.782257e+05 | 1.825113e+05 | 4.505025e+05 |
| Streptococcus | 2.691464e+05 | 1.016518e+05 | 2.668361e+05 | 5.313619e+04 | 1.420816e+05 | 2.310269e+05 | 8.201455e+04 | 2.425783e+04 | 3.130415e+05 | 1.155135e+05 | ... | 5.105695e+05 | 6.283932e+05 | 1.547880e+05 | 3.696431e+04 | 1.848215e+04 | 3.234377e+04 | 2.079242e+04 | 2.772323e+04 | 4.620538e+03 | 1.732702e+04 |
| Klebsiella | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | ... | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 |
| Enterococcus | 4.389511e+04 | 1.501675e+04 | 4.620538e+03 | 8.259212e+05 | 1.180548e+06 | 9.345039e+05 | 3.742636e+05 | 1.572138e+06 | 1.732702e+04 | 0.000000e+00 | ... | 2.841631e+05 | 7.947326e+05 | 1.330715e+06 | 1.505140e+06 | 1.421971e+06 | 1.443918e+05 | 8.744369e+05 | 3.696431e+05 | 9.321936e+05 | 3.892804e+05 |
| Pseudomonas | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | ... | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 3.465404e+03 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 3.465404e+03 | 4.620538e+03 | 0.000000e+00 |
| Lachnospiraceae_NK4A136_group | 1.212891e+05 | 1.201340e+05 | 1.432367e+05 | 0.000000e+00 | 1.155135e+03 | 0.000000e+00 | 6.642024e+05 | 5.775673e+03 | 4.042971e+04 | 3.465404e+04 | ... | 5.175003e+05 | 7.508375e+04 | 2.824304e+06 | 1.674945e+05 | 2.223634e+06 | 1.187478e+06 | 7.242694e+05 | 5.429132e+05 | 2.790805e+06 | 1.498210e+06 |
| Muribaculaceae | 0.000000e+00 | 0.000000e+00 | 2.310269e+03 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | ... | 8.940742e+05 | 5.232760e+05 | 7.970429e+04 | 7.554580e+05 | 3.465404e+03 | 2.238651e+06 | 2.984868e+06 | 4.469216e+06 | 3.465404e+03 | 0.000000e+00 |
10 rows × 90 columns
Sort metadata file¶
In [ ]:
P11_C_NEC_metadata = sort_meta(P11_C_NEC_metadata)
P11_C_NEC_metadata.head(10)
Out[Â ]:
| Experiment | |
|---|---|
| 050A | C |
| 085A | C |
| 086A | C |
| 087A | C |
| 088A | C |
| 095A | C |
| 097A | C |
| 102A | C |
| 103A | C |
| 104A | C |
Build color dictionary¶
In [ ]:
P11_C_NEC_color_dict,P11_C_NEC_row_colors = colordict(P11_C_NEC_metadata)
P11_C_NEC_color_dict
Out[Â ]:
{'C': 'g', 'NEC': 'blue'}
Build Heatmap Matrix¶
In [ ]:
P11_C_NEC_otus_data_heatmap = create_heatmap(P11_C_NEC_otus_data,P11_C_NEC_metadata)
P11_C_NEC_otus_data_heatmap.head(10)
Out[Â ]:
| 050A | 086A | 087A | 088A | 095A | 096A | 102A | 103A | 104A | 105A | ... | 071A | 070A | 069A | 068A | 217A | 218A | 219A | 221A | 223A | 248A | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Lactobacillus | 6.057526e+06 | 5.326326e+06 | 7.571907e+06 | 6.762158e+06 | 5.340187e+06 | 6.402911e+06 | 8.668130e+06 | 8.635786e+06 | 5.210812e+06 | 8.074391e+06 | ... | 6.512649e+06 | 8.744369e+05 | 4.429941e+06 | 6.722883e+06 | 2.529745e+05 | 2.079242e+05 | 1.386161e+04 | 3.868546e+06 | 2.262909e+06 | 1.845905e+06 |
| Escherichia_Shigella | 3.487351e+06 | 4.218551e+06 | 9.125563e+05 | 1.373455e+06 | 2.945593e+06 | 4.089176e+05 | 2.772323e+05 | 3.927458e+04 | 3.095761e+05 | 5.509992e+05 | ... | 2.194756e+04 | 1.386161e+04 | 1.386161e+04 | 5.775673e+03 | 5.186554e+05 | 2.161257e+06 | 6.091025e+06 | 1.333025e+06 | 1.692272e+06 | 2.952524e+06 |
| Muribacter | 2.079242e+04 | 2.772323e+04 | 9.010050e+04 | 1.674945e+05 | 4.620538e+03 | 3.003350e+04 | 4.019868e+05 | 5.879635e+05 | 2.503177e+06 | 5.255862e+05 | ... | 2.310269e+03 | 1.501675e+04 | 1.536329e+05 | 1.963729e+04 | 6.930807e+03 | 1.316853e+05 | 1.963729e+04 | 2.252512e+05 | 0.000000e+00 | 0.000000e+00 |
| Staphylococcus | 0.000000e+00 | 5.775673e+03 | 2.310269e+04 | 6.237727e+04 | 6.930807e+03 | 1.386161e+04 | 0.000000e+00 | 0.000000e+00 | 1.848215e+04 | 0.000000e+00 | ... | 1.157445e+06 | 1.617188e+04 | 2.633707e+05 | 1.371145e+06 | 8.085942e+04 | 2.760772e+05 | 8.779023e+04 | 3.419198e+05 | 3.118863e+05 | 4.505025e+05 |
| Streptococcus | 2.691464e+05 | 2.841631e+05 | 8.386277e+05 | 7.670094e+05 | 1.328405e+05 | 2.922490e+05 | 4.562782e+05 | 6.526510e+05 | 7.288899e+05 | 4.886219e+05 | ... | 9.703130e+04 | 9.472104e+04 | 4.158484e+04 | 2.136999e+05 | 1.039621e+04 | 1.386161e+04 | 2.310269e+03 | 4.031420e+05 | 1.386161e+04 | 1.732702e+04 |
| Klebsiella | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 4.135382e+05 | 2.172808e+06 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | ... | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 5.660159e+04 | 2.541296e+04 | 8.085942e+03 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 |
| Enterococcus | 4.389511e+04 | 4.851565e+04 | 1.201340e+05 | 2.541296e+04 | 3.003350e+04 | 3.003350e+04 | 2.656810e+04 | 4.389511e+04 | 3.141966e+05 | 4.273998e+04 | ... | 2.171653e+06 | 4.145778e+06 | 5.058334e+06 | 1.644912e+06 | 2.656810e+04 | 7.161834e+04 | 2.663740e+06 | 3.349890e+05 | 1.363059e+06 | 3.892804e+05 |
| Pseudomonas | 0.000000e+00 | 0.000000e+00 | 1.155135e+04 | 9.241077e+03 | 2.310269e+03 | 6.930807e+03 | 1.039621e+04 | 2.310269e+04 | 7.808710e+05 | 1.155135e+03 | ... | 3.465404e+04 | 9.703130e+04 | 3.696431e+04 | 2.194756e+04 | 2.310269e+03 | 1.270648e+04 | 8.085942e+03 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 |
| Lachnospiraceae_NK4A136_group | 1.212891e+05 | 1.848215e+04 | 2.772323e+04 | 6.122213e+04 | 4.158484e+04 | 4.273998e+04 | 1.259097e+05 | 8.085942e+03 | 2.656810e+04 | 9.125563e+04 | ... | 0.000000e+00 | 1.266027e+06 | 0.000000e+00 | 0.000000e+00 | 2.772323e+05 | 4.417235e+06 | 1.501675e+04 | 4.920873e+05 | 3.014901e+05 | 1.498210e+06 |
| Muribaculaceae | 0.000000e+00 | 1.039621e+04 | 5.660159e+04 | 1.247545e+05 | 6.907705e+05 | 3.003350e+04 | 5.775673e+03 | 0.000000e+00 | 0.000000e+00 | 2.772323e+04 | ... | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 3.539332e+06 | 5.775673e+03 | 0.000000e+00 | 8.894536e+05 | 0.000000e+00 | 0.000000e+00 |
10 rows × 90 columns
In [ ]:
title = "P11 Control vs NEC Colon (Genus)"
# plot_cluster_heatmap(P11_C_NEC_otus_data_heatmap,P11_C_NEC_color_dict,title)
plot_cluster_heatmap(P11_C_NEC_otus_data_heatmap,P11_C_NEC_color_dict,P11_C_NEC_row_colors,title)
P11_NEC_Non-NEC_TI¶
Load datasets¶
In [ ]:
# Load data
P11_C_NEC_otus_data_TI = pd.read_csv('./P11_NEC_Non-NEC/TI/tsv/data_normalized.csv',sep=',',index_col=0)
P11_C_NEC_metadata_TI = pd.read_csv('./P11_NEC_Non-NEC/TI/tsv/NEC_TI.csv',sep=',',index_col=0)
P11_C_NEC_otus_data_TI.head(10)
Out[Â ]:
| 050B | 051B | 052B | 053B | 054B | 055B | 056B | 057B | 058B | 059B | ... | 239B | 240B | 241B | 242B | 243B | 244B | 245B | 246B | 247B | 248B | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Lactobacillus | 2.894527e+06 | 4.794247e+05 | 4.746304e+06 | 1.178586e+05 | 4.135038e+05 | 3.835398e+05 | 7.391131e+04 | 1.238514e+05 | 3.671594e+06 | 6.947663e+06 | ... | 7.277267e+06 | 2.299241e+06 | 1.258490e+05 | 4.634439e+05 | 165801.03880 | 2.317219e+05 | 2.077507e+05 | 153815.42150 | 2.634838e+06 | 3.755493e+05 |
| Escherichia_Shigella | 6.771874e+06 | 8.419896e+06 | 4.662405e+06 | 3.266081e+06 | 4.622453e+06 | 5.113863e+05 | 6.949660e+06 | 5.025969e+06 | 5.063923e+06 | 1.965641e+06 | ... | 1.797843e+04 | 1.278466e+05 | 1.140631e+06 | 5.992809e+04 | 203755.49340 | 2.477028e+05 | 3.076308e+06 | 521374.35080 | 8.030364e+05 | 1.977627e+05 |
| Muribacter | 9.188973e+04 | 0.000000e+00 | 1.997603e+05 | 2.796644e+04 | 2.397123e+04 | 8.729525e+05 | 3.935278e+05 | 1.598082e+05 | 5.193767e+04 | 6.272473e+05 | ... | 1.254495e+06 | 8.030364e+05 | 0.000000e+00 | 0.000000e+00 | 37954.45465 | 6.991610e+04 | 5.393528e+04 | 485417.49900 | 5.493408e+05 | 3.276069e+05 |
| Staphylococcus | 0.000000e+00 | 3.795445e+04 | 0.000000e+00 | 1.677986e+05 | 2.592889e+06 | 1.779864e+06 | 9.908110e+05 | 3.795445e+05 | 1.398322e+04 | 2.197363e+04 | ... | 2.796644e+04 | 5.593288e+04 | 1.258490e+06 | 1.508190e+06 | 517379.14500 | 2.113464e+06 | 2.087495e+06 | 685177.78670 | 7.471035e+05 | 2.087495e+06 |
| Streptococcus | 9.188973e+04 | 5.393528e+04 | 2.357171e+05 | 4.794247e+04 | 9.988014e+04 | 6.352377e+05 | 2.796644e+04 | 2.816620e+05 | 6.012785e+05 | 2.916500e+05 | ... | 2.696764e+05 | 2.996404e+04 | 2.097483e+05 | 1.737915e+05 | 27966.44027 | 2.237315e+05 | 1.458250e+05 | 0.00000 | 1.058730e+05 | 1.957651e+05 |
| Klebsiella | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | ... | 0.000000e+00 | 8.989213e+04 | 8.589692e+04 | 6.991610e+04 | 135836.99560 | 2.596884e+04 | 4.994007e+04 | 65920.89493 | 3.196165e+04 | 7.191370e+04 |
| Enterococcus | 0.000000e+00 | 2.077507e+05 | 0.000000e+00 | 4.414702e+05 | 7.770675e+05 | 1.118658e+05 | 4.634439e+05 | 4.794247e+04 | 0.000000e+00 | 0.000000e+00 | ... | 0.000000e+00 | 5.793048e+04 | 2.491011e+06 | 3.915302e+05 | 163803.43590 | 7.331203e+05 | 5.972833e+05 | 97882.54095 | 9.268877e+05 | 3.855374e+05 |
| Pseudomonas | 9.788254e+04 | 5.173791e+05 | 9.788254e+04 | 3.449860e+06 | 7.950459e+05 | 2.754694e+06 | 5.693168e+05 | 2.147423e+06 | 2.656812e+05 | 6.392329e+04 | ... | 1.158610e+05 | 6.771874e+05 | 3.995206e+05 | 7.530963e+05 | 689172.99240 | 5.633240e+05 | 5.353576e+05 | 583300.04000 | 3.116260e+05 | 4.554535e+05 |
| Lachnospiraceae_NK4A136_group | 0.000000e+00 | 1.598082e+04 | 0.000000e+00 | 0.000000e+00 | 1.218538e+05 | 0.000000e+00 | 2.197363e+04 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | ... | 0.000000e+00 | 2.996404e+04 | 3.995206e+04 | 1.797843e+04 | 189772.27330 | 4.994007e+04 | 2.996404e+04 | 43947.26328 | 1.358370e+05 | 6.592089e+04 |
| Muribaculaceae | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | ... | 0.000000e+00 | 5.393528e+04 | 1.797843e+04 | 4.194966e+04 | 27966.44027 | 2.397123e+04 | 0.000000e+00 | 23971.23452 | 1.997603e+03 | 1.797843e+04 |
10 rows × 93 columns
Sort Metadata File¶
In [ ]:
P11_C_NEC_metadata_TI = sort_meta(P11_C_NEC_metadata_TI)
P11_C_NEC_metadata_TI.head(10)
Out[Â ]:
| Experiment | |
|---|---|
| 050B | C |
| 085B | C |
| 086B | C |
| 087B | C |
| 088B | C |
| 095B | C |
| 097B | C |
| 102B | C |
| 103B | C |
| 104B | C |
Build color dictionary¶
In [ ]:
P11_C_NEC_color_dict_TI,P11_C_NEC_row_colors_TI = colordict(P11_C_NEC_metadata_TI)
P11_C_NEC_color_dict_TI
Out[Â ]:
{'C': 'g', 'NEC': 'blue'}
Build Heatmap Matrix¶
In [ ]:
P11_C_NEC_otus_data_heatmap_TI = create_heatmap(P11_C_NEC_otus_data_TI,P11_C_NEC_metadata_TI)
P11_C_NEC_otus_data_heatmap_TI.head(10)
Out[Â ]:
| 050B | 085B | 086B | 087B | 088B | 095B | 097B | 102B | 103B | 104B | ... | 072B | 071B | 070B | 069B | 068B | 217B | 218B | 220B | 099B | 248B | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Lactobacillus | 2.894527e+06 | 6.935677e+06 | 7.375150e+06 | 9.125050e+06 | 8.162205e+06 | 5.463444e+06 | 6.835797e+06 | 6.817819e+06 | 4.864163e+06 | 7.878546e+06 | ... | 5.273672e+05 | 4.093088e+06 | 1.941670e+06 | 1.717938e+05 | 1.038753e+05 | 529364.76230 | 2.217339e+05 | 1.198562e+05 | 5.513384e+05 | 3.755493e+05 |
| Escherichia_Shigella | 6.771874e+06 | 3.995206e+03 | 2.121454e+06 | 0.000000e+00 | 9.988014e+03 | 2.207351e+06 | 0.000000e+00 | 7.990412e+03 | 0.000000e+00 | 3.455853e+05 | ... | 1.598082e+04 | 5.992809e+03 | 2.397123e+04 | 0.000000e+00 | 6.991610e+04 | 113863.36400 | 1.278466e+05 | 1.937675e+05 | 4.914103e+06 | 1.977627e+05 |
| Muribacter | 9.188973e+04 | 2.712745e+06 | 5.992809e+04 | 4.135038e+05 | 7.331203e+05 | 4.874151e+05 | 6.252497e+05 | 2.586896e+06 | 3.066320e+06 | 6.811826e+05 | ... | 6.572113e+05 | 9.588494e+04 | 1.638034e+05 | 8.050340e+05 | 7.351179e+05 | 962844.58650 | 4.854175e+05 | 1.310427e+06 | 0.000000e+00 | 3.276069e+05 |
| Staphylococcus | 0.000000e+00 | 5.193767e+04 | 1.997603e+03 | 9.988014e+03 | 9.988014e+03 | 2.197363e+04 | 4.594487e+04 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | ... | 1.971634e+06 | 2.952457e+06 | 7.071514e+05 | 4.424690e+06 | 8.469836e+05 | 273671.59410 | 4.832201e+06 | 1.140631e+06 | 4.202956e+06 | 2.087495e+06 |
| Streptococcus | 9.188973e+04 | 2.996404e+04 | 2.576908e+05 | 2.177387e+05 | 7.111466e+05 | 5.793048e+04 | 5.393528e+04 | 1.658010e+05 | 4.354774e+05 | 4.314822e+05 | ... | 1.502197e+06 | 1.658010e+05 | 1.578106e+05 | 5.952857e+05 | 3.355973e+05 | 115860.96680 | 1.118658e+05 | 8.989213e+04 | 2.397123e+04 | 1.957651e+05 |
| Klebsiella | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 1.516181e+06 | 2.393128e+06 | 0.000000e+00 | 1.040751e+06 | 0.000000e+00 | ... | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 133839.39270 | 4.394726e+04 | 7.191370e+04 | 5.992809e+03 | 7.191370e+04 |
| Enterococcus | 0.000000e+00 | 0.000000e+00 | 1.997603e+03 | 0.000000e+00 | 0.000000e+00 | 9.988014e+03 | 0.000000e+00 | 0.000000e+00 | 1.398322e+04 | 2.457052e+05 | ... | 1.260487e+06 | 1.186576e+06 | 5.946864e+06 | 2.848582e+06 | 4.646424e+06 | 69916.10068 | 1.917699e+05 | 7.990412e+04 | 1.997603e+04 | 3.855374e+05 |
| Pseudomonas | 9.788254e+04 | 9.788254e+04 | 6.192569e+04 | 6.592089e+04 | 1.338394e+05 | 3.995206e+04 | 1.598082e+04 | 2.277267e+05 | 3.795445e+05 | 1.598082e+04 | ... | 1.622054e+06 | 4.115062e+05 | 5.753096e+05 | 5.033959e+05 | 1.823811e+06 | 383539.75230 | 2.477028e+05 | 1.837795e+05 | 5.393528e+04 | 4.554535e+05 |
| Lachnospiraceae_NK4A136_group | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 1.458250e+05 | ... | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 59928.08630 | 0.000000e+00 | 7.191370e+04 | 0.000000e+00 | 6.592089e+04 |
| Muribaculaceae | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | ... | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 17978.42589 | 2.197363e+04 | 3.595685e+04 | 0.000000e+00 | 1.797843e+04 |
10 rows × 93 columns
In [ ]:
title = "P11 Control vs NEC TI (Genus)"
# plot_cluster_heatmap(P11_C_NEC_otus_data_heatmap,P11_C_NEC_color_dict,title)
plot_cluster_heatmap(P11_C_NEC_otus_data_heatmap_TI,P11_C_NEC_color_dict_TI,P11_C_NEC_row_colors_TI,title)
/home/xuan/miniconda3/envs/xuan_cuda/lib/python3.10/site-packages/seaborn/matrix.py:560: UserWarning: Clustering large matrix with scipy. Installing `fastcluster` may give better performance. warnings.warn(msg)
P8 CONTROL WT HOMO HET¶
In [ ]:
# Load data
P8_C_gt_otus_data = pd.read_csv('Different_Time_Points/P8/CONTROL_WT_HOMO_HET/TSV/data_normalized.txt',sep='\t',index_col=0)
P8_C_gt_metadata = pd.read_csv('Different_Time_Points/P8/CONTROL_WT_HOMO_HET/TSV/P8_C_H_H.txt',sep='\t',index_col=0)
P8_C_gt_otus_data.head(10)
Out[Â ]:
| 1 | 2 | 3 | 7 | 8 | 11 | 13 | 14 | 15 | 22 | ... | 25 | 30 | 40 | 41 | 42 | 43 | 46 | 47 | 48 | 49 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Lactobacillus | 8.900945e+06 | 8.575440e+06 | 8.748320e+06 | 8.940053e+06 | 8.764565e+06 | 6.213674e+06 | 5.237360e+06 | 5.450352e+06 | 5.784481e+06 | 5.553439e+06 | ... | 6.827781e+06 | 6.886946e+06 | 5.275065e+06 | 7.159905e+06 | 6.710455e+06 | 5.290708e+06 | 5.419266e+06 | 5.249794e+06 | 8.040753e+06 | 7.526123e+06 |
| Escherichia_Shigella | 1.363791e+04 | 1.444014e+04 | 9.626762e+03 | 1.562343e+05 | 1.183290e+04 | 3.427729e+06 | 4.378974e+06 | 4.289124e+06 | 3.975452e+06 | 4.078338e+06 | ... | 2.948597e+06 | 2.859550e+06 | 4.323419e+06 | 1.943603e+06 | 2.960029e+06 | 4.365937e+06 | 4.150739e+06 | 4.366539e+06 | 5.709873e+05 | 1.739035e+06 |
| Muribacter | 1.648583e+05 | 1.335713e+05 | 1.750867e+05 | 1.353763e+05 | 2.791761e+05 | 4.151541e+04 | 1.540282e+05 | 4.993883e+04 | 6.277451e+04 | 1.333708e+05 | ... | 2.226189e+04 | 7.420629e+04 | 3.890816e+04 | 9.105313e+04 | 4.893604e+04 | 3.369367e+04 | 7.520908e+04 | 7.741521e+04 | 3.299172e+05 | 1.658611e+05 |
| Staphylococcus | 1.774934e+05 | 4.346082e+05 | 3.457612e+05 | 2.206133e+03 | 2.206133e+03 | 1.664628e+04 | 4.672991e+04 | 1.022844e+04 | 1.203345e+04 | 5.013939e+03 | ... | 2.807806e+03 | 0.000000e+00 | 3.208921e+03 | 2.607248e+03 | 3.610036e+03 | 3.409478e+03 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 |
| Streptococcus | 5.653717e+05 | 5.888370e+05 | 5.298730e+05 | 5.780069e+05 | 7.546981e+05 | 9.747097e+04 | 7.440685e+04 | 5.655723e+04 | 7.139849e+04 | 1.076994e+05 | ... | 1.109083e+05 | 1.472092e+05 | 2.310423e+05 | 2.807806e+05 | 1.768918e+05 | 2.081787e+05 | 2.246245e+05 | 2.252261e+05 | 8.944867e+05 | 4.113435e+05 |
| Enterococcus | 9.827320e+03 | 2.206133e+03 | 1.002788e+03 | 3.610036e+03 | 3.208921e+03 | 1.403903e+03 | 1.203345e+03 | 1.002788e+03 | 1.805018e+03 | 0.000000e+00 | ... | 8.022302e+02 | 0.000000e+00 | 1.604460e+03 | 5.013939e+03 | 6.217284e+03 | 1.363791e+04 | 1.203345e+03 | 1.805018e+03 | 1.805018e+03 | 2.607248e+03 |
| Lachnospiraceae_NK4A136_group | 5.214496e+03 | 1.123122e+04 | 1.303624e+04 | 1.203345e+04 | 1.223401e+04 | 8.423417e+03 | 7.420629e+03 | 1.383847e+04 | 4.011151e+03 | 5.615611e+03 | ... | 4.412266e+03 | 1.403903e+03 | 7.621187e+03 | 2.807806e+04 | 5.214496e+03 | 6.217284e+03 | 1.022844e+04 | 6.016726e+02 | 1.383847e+04 | 9.225647e+03 |
| Muribaculaceae | 3.028419e+04 | 5.976615e+04 | 3.389423e+04 | 4.552656e+04 | 3.790538e+04 | 5.715890e+04 | 3.670203e+04 | 2.226189e+04 | 2.386635e+04 | 2.186077e+04 | ... | 2.045687e+04 | 4.211709e+03 | 3.168809e+04 | 1.293596e+05 | 1.865185e+04 | 2.527025e+04 | 2.466858e+04 | 2.286356e+04 | 4.131486e+04 | 4.512545e+04 |
| Lachnospiraceae | 1.002788e+03 | 5.214496e+03 | 2.807806e+03 | 5.214496e+03 | 1.805018e+03 | 2.807806e+03 | 1.203345e+03 | 6.016726e+02 | 2.607248e+03 | 2.206133e+03 | ... | 0.000000e+00 | 0.000000e+00 | 2.406691e+03 | 1.203345e+04 | 2.005575e+03 | 2.807806e+03 | 3.810593e+03 | 2.807806e+03 | 3.208921e+03 | 1.805018e+03 |
| Bacteroides | 7.821744e+03 | 7.220072e+03 | 9.426205e+03 | 6.417842e+03 | 5.615611e+03 | 7.821744e+03 | 3.610036e+03 | 7.019514e+03 | 2.206133e+03 | 6.818957e+03 | ... | 3.008363e+03 | 0.000000e+00 | 5.013939e+03 | 1.383847e+04 | 2.807806e+03 | 1.805018e+03 | 1.203345e+04 | 4.011151e+03 | 6.818957e+03 | 5.013939e+03 |
10 rows × 22 columns
In [ ]:
def format_meta(metadata,condition ='Experiment'):
metadata[condition] = metadata[condition].str.rstrip()
return metadata
P8_C_gt_metadata = format_meta(P8_C_gt_metadata,"Genotype")
In [ ]:
P8_C_gt_metadata.head(10)
Out[Â ]:
| Experiment | Tissue type | DOL | Genotype | |
|---|---|---|---|---|
| 1 | C | colon | DOL8 | Sig Homo |
| 2 | C | colon | DOL8 | Sig WT |
| 3 | C | colon | DOL8 | Sig Het |
| 7 | C | colon | DOL8 | Sig Homo |
| 8 | C | colon | DOL8 | Sig Homo |
| 11 | C | colon | DOL8 | Sig WT |
| 12 | C | colon | DOL8 | Sig Het |
| 13 | C | colon | DOL8 | Sig Homo |
| 14 | C | colon | DOL8 | Sig Het |
| 15 | C | colon | DOL8 | Sig Het |
In [ ]:
def genotypecolordict(metadata,condition ='Experiment'):
# metadata = metadata.sort_values(condition,ascending=False)
# metadata[condition] = metadata[condition].str.replace(' ', '')
color_dict=dict(zip(np.unique(metadata[condition]),np.array(['g','blue','violet'])))
# print(metadata[condition])
row_colors = metadata[condition].map(color_dict)
# print(metadata.index)
return color_dict,row_colors
In [ ]:
def sort_meta_GT(P8_C_F_metadata,condition = "Experiment"):
new_meta_index = []
for index1 in P8_C_F_metadata.index:
new_meta_index.append(str(index1))
P8_C_F_metadata.index = new_meta_index
# metadata = P8_C_F_metadata.sort_values(condition,ascending=False)
# metadata = P8_C_F_metadata.sort_values(condition,ascending=False)
return P8_C_F_metadata
P8_C_metadata_gt= sort_meta_GT(P8_C_gt_metadata,"Genotype")
P8_C_metadata_gt.head(5)
Out[Â ]:
| Experiment | Tissue type | DOL | Genotype | |
|---|---|---|---|---|
| 1 | C | colon | DOL8 | Sig Homo |
| 2 | C | colon | DOL8 | Sig WT |
| 3 | C | colon | DOL8 | Sig Het |
| 7 | C | colon | DOL8 | Sig Homo |
| 8 | C | colon | DOL8 | Sig Homo |
In [ ]:
def create_heatmap(otus_data,metadata,condition ="Experiment" ):
heatmap = otus_data
otus_data = otus_data.transpose()
new_column = []
new_idx = []
for index1 in otus_data.index:
new_idx.append(str(index1).rstrip())
otus_data.index = new_idx
# print(new_idx)
for index1 in otus_data.index:
for index2 in metadata.index:
value = metadata.loc[index2, condition]
if str(index1) == str(index2):
new_column.append(value)
# print(new_column)
otus_data[condition] = new_column
# print(otus_data[condition])
otus_data = otus_data.sort_values(by=condition,ascending=False)
# print(otus_data[condition])
heatmap = otus_data.drop(columns=[condition])
heatmap = heatmap.transpose()
return heatmap
P8_C_otus_data_heatmap = create_heatmap(P8_C_gt_otus_data,P8_C_metadata_gt,"Genotype")
P8_C_otus_data_heatmap.head(5)
Out[Â ]:
| 2 | 46 | 11 | 40 | 22 | 25 | 1 | 47 | 42 | 41 | ... | 23 | 13 | 8 | 7 | 15 | 14 | 43 | 3 | 48 | 49 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Lactobacillus | 8.575440e+06 | 5.419266e+06 | 6.213674e+06 | 5.275065e+06 | 5.553439e+06 | 6.827781e+06 | 8.900945e+06 | 5.249794e+06 | 6.710455e+06 | 7.159905e+06 | ... | 6.614589e+06 | 5.237360e+06 | 8.764565e+06 | 8.940053e+06 | 5.784481e+06 | 5.450352e+06 | 5.290708e+06 | 8.748320e+06 | 8.040753e+06 | 7.526123e+06 |
| Escherichia_Shigella | 1.444014e+04 | 4.150739e+06 | 3.427729e+06 | 4.323419e+06 | 4.078338e+06 | 2.948597e+06 | 1.363791e+04 | 4.366539e+06 | 2.960029e+06 | 1.943603e+06 | ... | 3.074547e+06 | 4.378974e+06 | 1.183290e+04 | 1.562343e+05 | 3.975452e+06 | 4.289124e+06 | 4.365937e+06 | 9.626762e+03 | 5.709873e+05 | 1.739035e+06 |
| Muribacter | 1.335713e+05 | 7.520908e+04 | 4.151541e+04 | 3.890816e+04 | 1.333708e+05 | 2.226189e+04 | 1.648583e+05 | 7.741521e+04 | 4.893604e+04 | 9.105313e+04 | ... | 4.512545e+04 | 1.540282e+05 | 2.791761e+05 | 1.353763e+05 | 6.277451e+04 | 4.993883e+04 | 3.369367e+04 | 1.750867e+05 | 3.299172e+05 | 1.658611e+05 |
| Staphylococcus | 4.346082e+05 | 0.000000e+00 | 1.664628e+04 | 3.208921e+03 | 5.013939e+03 | 2.807806e+03 | 1.774934e+05 | 0.000000e+00 | 3.610036e+03 | 2.607248e+03 | ... | 1.203345e+03 | 4.672991e+04 | 2.206133e+03 | 2.206133e+03 | 1.203345e+04 | 1.022844e+04 | 3.409478e+03 | 3.457612e+05 | 0.000000e+00 | 0.000000e+00 |
| Streptococcus | 5.888370e+05 | 2.246245e+05 | 9.747097e+04 | 2.310423e+05 | 1.076994e+05 | 1.109083e+05 | 5.653717e+05 | 2.252261e+05 | 1.768918e+05 | 2.807806e+05 | ... | 1.895269e+05 | 7.440685e+04 | 7.546981e+05 | 5.780069e+05 | 7.139849e+04 | 5.655723e+04 | 2.081787e+05 | 5.298730e+05 | 8.944867e+05 | 4.113435e+05 |
5 rows × 22 columns
In [ ]:
def plot_cluster_heatmap(heatmap,color_dict,row_colors,title):
# print(row_colors.index)
custom_cmap = sns.color_palette("OrRd", as_cmap=True)
hm = sns.clustermap(heatmap,
metric="correlation",
standard_scale=0,
z_score=None,
col_colors=row_colors,
col_cluster=False,
cmap=custom_cmap,
# cbar_pos=(0, .2, .03, .4),
figsize=(14, 12))
# Create a color legend using the color_dict
legend_labels = [f"{experiment}" for experiment, color in color_dict.items()]
legend_colors = [color for _, color in color_dict.items()]
# print(legend_colors)
legend_handles = [plt.Line2D([0], [0], marker='o', color='w', label=label, markersize=10, markerfacecolor=color) for label, color in zip(legend_labels, legend_colors)]
plt.legend(handles=legend_handles, title="Genotype", bbox_to_anchor=(15, 1), loc='upper left')
# Add a title to the center of the heatmap
ax = hm.ax_heatmap
ax.text(0.5, 1.1, title, fontsize=12, ha="center", va="center", transform=ax.transAxes)
# Get the current Axes objects
ax_row_labels = hm.ax_row_dendrogram
ax_col_labels = hm.ax_col_dendrogram
# Set row and column labels font size
row_font_size = 4
col_font_size = 4
for label in ax_row_labels.get_yticklabels():
label.set_fontsize(row_font_size)
for label in ax_col_labels.get_xticklabels():
label.set_fontsize(col_font_size)
# Display the plot
plt.show()
title = "P8 Control Genotype WT vs Homo vs Het"
P8_C_color_dict_gt,P8_C_row_colors_gt = genotypecolordict(P8_C_gt_metadata,"Genotype")
plot_cluster_heatmap(P8_C_otus_data_heatmap,P8_C_color_dict_gt,P8_C_row_colors_gt,title)
P8 CONTROL WT HOMO HET¶
In [ ]:
# Load data
P8_C_gt_hh = pd.read_csv('Different_Time_Points/P8/CONTROL_WT_HOMOHET/TSV/data_normalized.txt',sep='\t',index_col=0)
P8_C_gt_metadata_hh = pd.read_csv('Different_Time_Points/P8/CONTROL_WT_HOMOHET/TSV/P8_C_Het+Homo.txt',sep='\t',index_col=0)
P8_C_gt_hh.head(10)
Out[Â ]:
| 1 | 2 | 3 | 7 | 8 | 11 | 13 | 14 | 15 | 22 | ... | 25 | 30 | 40 | 41 | 42 | 43 | 46 | 47 | 48 | 49 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Lactobacillus | 8.900945e+06 | 8.575440e+06 | 8.748320e+06 | 8.940053e+06 | 8.764565e+06 | 6.213674e+06 | 5.237360e+06 | 5.450352e+06 | 5.784481e+06 | 5.553439e+06 | ... | 6.827781e+06 | 6.886946e+06 | 5.275065e+06 | 7.159905e+06 | 6.710455e+06 | 5.290708e+06 | 5.419266e+06 | 5.249794e+06 | 8.040753e+06 | 7.526123e+06 |
| Escherichia_Shigella | 1.363791e+04 | 1.444014e+04 | 9.626762e+03 | 1.562343e+05 | 1.183290e+04 | 3.427729e+06 | 4.378974e+06 | 4.289124e+06 | 3.975452e+06 | 4.078338e+06 | ... | 2.948597e+06 | 2.859550e+06 | 4.323419e+06 | 1.943603e+06 | 2.960029e+06 | 4.365937e+06 | 4.150739e+06 | 4.366539e+06 | 5.709873e+05 | 1.739035e+06 |
| Muribacter | 1.648583e+05 | 1.335713e+05 | 1.750867e+05 | 1.353763e+05 | 2.791761e+05 | 4.151541e+04 | 1.540282e+05 | 4.993883e+04 | 6.277451e+04 | 1.333708e+05 | ... | 2.226189e+04 | 7.420629e+04 | 3.890816e+04 | 9.105313e+04 | 4.893604e+04 | 3.369367e+04 | 7.520908e+04 | 7.741521e+04 | 3.299172e+05 | 1.658611e+05 |
| Staphylococcus | 1.774934e+05 | 4.346082e+05 | 3.457612e+05 | 2.206133e+03 | 2.206133e+03 | 1.664628e+04 | 4.672991e+04 | 1.022844e+04 | 1.203345e+04 | 5.013939e+03 | ... | 2.807806e+03 | 0.000000e+00 | 3.208921e+03 | 2.607248e+03 | 3.610036e+03 | 3.409478e+03 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 |
| Streptococcus | 5.653717e+05 | 5.888370e+05 | 5.298730e+05 | 5.780069e+05 | 7.546981e+05 | 9.747097e+04 | 7.440685e+04 | 5.655723e+04 | 7.139849e+04 | 1.076994e+05 | ... | 1.109083e+05 | 1.472092e+05 | 2.310423e+05 | 2.807806e+05 | 1.768918e+05 | 2.081787e+05 | 2.246245e+05 | 2.252261e+05 | 8.944867e+05 | 4.113435e+05 |
| Enterococcus | 9.827320e+03 | 2.206133e+03 | 1.002788e+03 | 3.610036e+03 | 3.208921e+03 | 1.403903e+03 | 1.203345e+03 | 1.002788e+03 | 1.805018e+03 | 0.000000e+00 | ... | 8.022302e+02 | 0.000000e+00 | 1.604460e+03 | 5.013939e+03 | 6.217284e+03 | 1.363791e+04 | 1.203345e+03 | 1.805018e+03 | 1.805018e+03 | 2.607248e+03 |
| Lachnospiraceae_NK4A136_group | 5.214496e+03 | 1.123122e+04 | 1.303624e+04 | 1.203345e+04 | 1.223401e+04 | 8.423417e+03 | 7.420629e+03 | 1.383847e+04 | 4.011151e+03 | 5.615611e+03 | ... | 4.412266e+03 | 1.403903e+03 | 7.621187e+03 | 2.807806e+04 | 5.214496e+03 | 6.217284e+03 | 1.022844e+04 | 6.016726e+02 | 1.383847e+04 | 9.225647e+03 |
| Muribaculaceae | 3.028419e+04 | 5.976615e+04 | 3.389423e+04 | 4.552656e+04 | 3.790538e+04 | 5.715890e+04 | 3.670203e+04 | 2.226189e+04 | 2.386635e+04 | 2.186077e+04 | ... | 2.045687e+04 | 4.211709e+03 | 3.168809e+04 | 1.293596e+05 | 1.865185e+04 | 2.527025e+04 | 2.466858e+04 | 2.286356e+04 | 4.131486e+04 | 4.512545e+04 |
| Lachnospiraceae | 1.002788e+03 | 5.214496e+03 | 2.807806e+03 | 5.214496e+03 | 1.805018e+03 | 2.807806e+03 | 1.203345e+03 | 6.016726e+02 | 2.607248e+03 | 2.206133e+03 | ... | 0.000000e+00 | 0.000000e+00 | 2.406691e+03 | 1.203345e+04 | 2.005575e+03 | 2.807806e+03 | 3.810593e+03 | 2.807806e+03 | 3.208921e+03 | 1.805018e+03 |
| Bacteroides | 7.821744e+03 | 7.220072e+03 | 9.426205e+03 | 6.417842e+03 | 5.615611e+03 | 7.821744e+03 | 3.610036e+03 | 7.019514e+03 | 2.206133e+03 | 6.818957e+03 | ... | 3.008363e+03 | 0.000000e+00 | 5.013939e+03 | 1.383847e+04 | 2.807806e+03 | 1.805018e+03 | 1.203345e+04 | 4.011151e+03 | 6.818957e+03 | 5.013939e+03 |
10 rows × 22 columns
In [ ]:
P8_C_gt_metadata_hh = format_meta(P8_C_gt_metadata_hh,"Genotype")
In [ ]:
P8_C_metadata_gt_hh= sort_meta_GT(P8_C_gt_metadata_hh,"Genotype")
P8_C_metadata_gt_hh.head(5)
Out[Â ]:
| Experiment | Tissue type | DOL | Genotype | |
|---|---|---|---|---|
| 1 | C | colon | DOL8 | Sig Homo_HET |
| 2 | C | colon | DOL8 | Sig WT |
| 3 | C | colon | DOL8 | Sig Homo_HET |
| 7 | C | colon | DOL8 | Sig Homo_HET |
| 8 | C | colon | DOL8 | Sig Homo_HET |
In [ ]:
P8_C_hh_data_heatmap = create_heatmap(P8_C_gt_otus_data,P8_C_metadata_gt,"Genotype")
P8_C_hh_data_heatmap.head(5)
Out[Â ]:
| 2 | 46 | 11 | 40 | 22 | 25 | 1 | 47 | 42 | 41 | ... | 23 | 13 | 8 | 7 | 15 | 14 | 43 | 3 | 48 | 49 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Lactobacillus | 8.575440e+06 | 5.419266e+06 | 6.213674e+06 | 5.275065e+06 | 5.553439e+06 | 6.827781e+06 | 8.900945e+06 | 5.249794e+06 | 6.710455e+06 | 7.159905e+06 | ... | 6.614589e+06 | 5.237360e+06 | 8.764565e+06 | 8.940053e+06 | 5.784481e+06 | 5.450352e+06 | 5.290708e+06 | 8.748320e+06 | 8.040753e+06 | 7.526123e+06 |
| Escherichia_Shigella | 1.444014e+04 | 4.150739e+06 | 3.427729e+06 | 4.323419e+06 | 4.078338e+06 | 2.948597e+06 | 1.363791e+04 | 4.366539e+06 | 2.960029e+06 | 1.943603e+06 | ... | 3.074547e+06 | 4.378974e+06 | 1.183290e+04 | 1.562343e+05 | 3.975452e+06 | 4.289124e+06 | 4.365937e+06 | 9.626762e+03 | 5.709873e+05 | 1.739035e+06 |
| Muribacter | 1.335713e+05 | 7.520908e+04 | 4.151541e+04 | 3.890816e+04 | 1.333708e+05 | 2.226189e+04 | 1.648583e+05 | 7.741521e+04 | 4.893604e+04 | 9.105313e+04 | ... | 4.512545e+04 | 1.540282e+05 | 2.791761e+05 | 1.353763e+05 | 6.277451e+04 | 4.993883e+04 | 3.369367e+04 | 1.750867e+05 | 3.299172e+05 | 1.658611e+05 |
| Staphylococcus | 4.346082e+05 | 0.000000e+00 | 1.664628e+04 | 3.208921e+03 | 5.013939e+03 | 2.807806e+03 | 1.774934e+05 | 0.000000e+00 | 3.610036e+03 | 2.607248e+03 | ... | 1.203345e+03 | 4.672991e+04 | 2.206133e+03 | 2.206133e+03 | 1.203345e+04 | 1.022844e+04 | 3.409478e+03 | 3.457612e+05 | 0.000000e+00 | 0.000000e+00 |
| Streptococcus | 5.888370e+05 | 2.246245e+05 | 9.747097e+04 | 2.310423e+05 | 1.076994e+05 | 1.109083e+05 | 5.653717e+05 | 2.252261e+05 | 1.768918e+05 | 2.807806e+05 | ... | 1.895269e+05 | 7.440685e+04 | 7.546981e+05 | 5.780069e+05 | 7.139849e+04 | 5.655723e+04 | 2.081787e+05 | 5.298730e+05 | 8.944867e+05 | 4.113435e+05 |
5 rows × 22 columns
In [ ]:
title = "P8 Control Genotype WT vs Homo_HET"
P8_C_color_dict_gt_hh,P8_C_row_colors_gt_hh = genotypecolordict(P8_C_metadata_gt_hh,"Genotype")
plot_cluster_heatmap(P8_C_hh_data_heatmap,P8_C_color_dict_gt_hh,P8_C_row_colors_gt_hh,title)
P8 Feeding WT HOMO HET¶
In [ ]:
# Load data
P8_F_gt_h_h = pd.read_csv('Different_Time_Points/P8/FEEDING_WT_HOMO_HET/TSV/data_normalized.txt',sep='\t',index_col=0)
P8_F_gt_metadata_h_h = pd.read_csv('Different_Time_Points/P8/FEEDING_WT_HOMO_HET/TSV/P8_F_H_H.txt',sep='\t',index_col=0)
P8_F_gt_h_h.head(10)
Out[Â ]:
| 4 | 5 | 6 | 9 | 10 | 16 | 17 | 18 | 19 | 20 | ... | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 44 | 45 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Lactobacillus | 2.757596e+05 | 7.052214e+05 | 2.520262e+05 | 1.469857e+06 | 5.145468e+05 | 4.352740e+05 | 1.942265e+05 | 1.465013e+06 | 5.038910e+05 | 3.233556e+06 | ... | 2.221576e+06 | 5.631438e+05 | 8.363202e+04 | 4.975136e+06 | 5.505667e+06 | 4.188059e+05 | 7.666538e+06 | 7.145210e+06 | 3.528206e+06 | 7.570474e+05 |
| Escherichia_Shigella | 8.785398e+06 | 7.533017e+06 | 7.648132e+06 | 7.174271e+06 | 7.895476e+06 | 9.028545e+06 | 8.464594e+06 | 8.180116e+06 | 8.602958e+06 | 5.924634e+06 | ... | 7.336046e+06 | 8.683038e+06 | 8.799445e+06 | 2.421777e+04 | 1.291614e+04 | 1.002616e+05 | 1.501502e+04 | 1.727534e+04 | 5.471116e+06 | 8.344796e+06 |
| Muribacter | 7.265330e+03 | 2.723691e+05 | 8.347057e+04 | 5.392489e+04 | 2.216733e+05 | 1.372340e+04 | 1.291614e+04 | 1.227033e+04 | 3.180600e+04 | 5.973716e+03 | ... | 1.517647e+04 | 4.843553e+03 | 2.906132e+04 | 2.269528e+06 | 9.530498e+05 | 5.574607e+06 | 4.084730e+04 | 6.120637e+05 | 1.356195e+04 | 1.888986e+04 |
| Staphylococcus | 1.414318e+05 | 8.761988e+05 | 7.265330e+04 | 7.593077e+05 | 1.004069e+06 | 8.411637e+04 | 6.813265e+04 | 1.248022e+05 | 1.906745e+05 | 1.018761e+05 | ... | 1.937421e+03 | 5.005005e+03 | 0.000000e+00 | 3.137008e+05 | 1.653751e+06 | 1.076883e+05 | 7.234654e+05 | 1.888986e+05 | 2.599374e+04 | 1.243179e+04 |
| Streptococcus | 6.780975e+03 | 5.553941e+04 | 2.502503e+04 | 2.437922e+04 | 8.944428e+04 | 1.033291e+04 | 2.437922e+04 | 2.550938e+04 | 2.599374e+04 | 2.276470e+04 | ... | 8.718396e+03 | 1.146308e+04 | 8.072589e+02 | 4.903290e+05 | 6.445155e+05 | 4.294617e+04 | 1.438535e+05 | 4.062127e+05 | 1.855081e+05 | 7.555943e+04 |
| Enterococcus | 1.388485e+04 | 5.489360e+03 | 3.600375e+04 | 1.178598e+04 | 8.556944e+03 | 5.166457e+03 | 1.210888e+04 | 2.906132e+03 | 6.619523e+03 | 4.197746e+03 | ... | 4.682101e+03 | 7.265330e+03 | 1.775970e+04 | 3.342052e+04 | 1.372340e+04 | 3.923278e+04 | 2.357196e+04 | 2.405631e+04 | 8.072589e+03 | 1.049437e+04 |
| Pseudomonas | 0.000000e+00 | 0.000000e+00 | 1.453066e+03 | 0.000000e+00 | 4.843553e+02 | 0.000000e+00 | 1.453066e+03 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | ... | 3.229035e+02 | 3.229035e+03 | 0.000000e+00 | 4.359198e+03 | 1.291614e+03 | 3.229035e+03 | 2.260325e+03 | 0.000000e+00 | 1.614518e+03 | 4.843553e+02 |
| Lachnospiraceae_NK4A136_group | 4.262327e+04 | 2.776971e+04 | 1.076883e+05 | 3.212890e+04 | 1.291614e+04 | 2.179599e+04 | 6.215893e+04 | 9.364203e+03 | 2.954567e+04 | 4.343053e+04 | ... | 1.840550e+04 | 3.487358e+04 | 5.666957e+04 | 9.574090e+04 | 6.054442e+04 | 1.993929e+05 | 7.055443e+04 | 7.507508e+04 | 4.100875e+04 | 4.601376e+04 |
| Muribaculaceae | 1.879299e+05 | 1.356195e+05 | 4.259098e+05 | 1.407859e+05 | 6.684103e+04 | 9.477219e+04 | 2.881914e+05 | 4.601376e+04 | 1.396558e+05 | 1.587071e+05 | ... | 8.702251e+04 | 1.779199e+05 | 2.973942e+05 | 4.654655e+05 | 2.161839e+05 | 7.723853e+05 | 3.153153e+05 | 4.063741e+05 | 1.948723e+05 | 1.829249e+05 |
| Lachnospiraceae | 1.953566e+04 | 8.234040e+03 | 4.795118e+04 | 9.687106e+03 | 2.583228e+03 | 9.848558e+03 | 3.777972e+04 | 2.260325e+03 | 1.888986e+04 | 1.566082e+04 | ... | 8.718396e+03 | 2.098873e+04 | 3.358197e+04 | 4.730537e+04 | 2.825406e+04 | 1.273854e+05 | 3.745681e+04 | 4.536795e+04 | 2.034292e+04 | 2.615519e+04 |
10 rows × 26 columns
In [ ]:
P8_F_gt_metadata_h_h = format_meta(P8_F_gt_metadata_h_h,"Genotype")
In [ ]:
P8_F_gt_metadata_h_h.head(5)
Out[Â ]:
| Experiment | Tissue type | DOL | Genotype | |
|---|---|---|---|---|
| 4 | F | colon | DOL8 | Sig Homo |
| 5 | F | colon | DOL8 | Sig WT |
| 6 | F | colon | DOL8 | Sig HET |
| 9 | F | colon | DOL8 | Sig HET |
| 10 | F | colon | DOL8 | Sig WT |
In [ ]:
P8_F_metadata_gt_h_h= sort_meta_GT(P8_F_gt_metadata_h_h,"Genotype")
P8_F_metadata_gt_h_h.head(5)
Out[Â ]:
| Experiment | Tissue type | DOL | Genotype | |
|---|---|---|---|---|
| 4 | F | colon | DOL8 | Sig Homo |
| 5 | F | colon | DOL8 | Sig WT |
| 6 | F | colon | DOL8 | Sig HET |
| 9 | F | colon | DOL8 | Sig HET |
| 10 | F | colon | DOL8 | Sig WT |
In [ ]:
P8_F_h_h_data_heatmap = create_heatmap(P8_F_gt_h_h,P8_F_metadata_gt_h_h,"Genotype")
P8_F_h_h_data_heatmap.head(5)
Out[Â ]:
| 27 | 44 | 39 | 10 | 36 | 18 | 32 | 20 | 5 | 4 | ... | 31 | 19 | 33 | 34 | 35 | 17 | 38 | 9 | 6 | 28 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Lactobacillus | 3.677871e+05 | 3.528206e+06 | 7.145210e+06 | 5.145468e+05 | 5.505667e+06 | 1.465013e+06 | 2.221576e+06 | 3.233556e+06 | 7.052214e+05 | 2.757596e+05 | ... | 4.552940e+04 | 5.038910e+05 | 5.631438e+05 | 8.363202e+04 | 4.975136e+06 | 1.942265e+05 | 7.666538e+06 | 1.469857e+06 | 2.520262e+05 | 1.468888e+06 |
| Escherichia_Shigella | 8.919403e+06 | 5.471116e+06 | 1.727534e+04 | 7.895476e+06 | 1.291614e+04 | 8.180116e+06 | 7.336046e+06 | 5.924634e+06 | 7.533017e+06 | 8.785398e+06 | ... | 9.122510e+06 | 8.602958e+06 | 8.683038e+06 | 8.799445e+06 | 2.421777e+04 | 8.464594e+06 | 1.501502e+04 | 7.174271e+06 | 7.648132e+06 | 7.740644e+06 |
| Muribacter | 1.905131e+04 | 1.356195e+04 | 6.120637e+05 | 2.216733e+05 | 9.530498e+05 | 1.227033e+04 | 1.517647e+04 | 5.973716e+03 | 2.723691e+05 | 7.265330e+03 | ... | 5.327909e+03 | 3.180600e+04 | 4.843553e+03 | 2.906132e+04 | 2.269528e+06 | 1.291614e+04 | 4.084730e+04 | 5.392489e+04 | 8.347057e+04 | 1.614518e+03 |
| Staphylococcus | 1.650037e+05 | 2.599374e+04 | 1.888986e+05 | 1.004069e+06 | 1.653751e+06 | 1.248022e+05 | 1.937421e+03 | 1.018761e+05 | 8.761988e+05 | 1.414318e+05 | ... | 1.775970e+03 | 1.906745e+05 | 5.005005e+03 | 0.000000e+00 | 3.137008e+05 | 6.813265e+04 | 7.234654e+05 | 7.593077e+05 | 7.265330e+04 | 1.559624e+05 |
| Streptococcus | 1.727534e+04 | 1.855081e+05 | 4.062127e+05 | 8.944428e+04 | 6.445155e+05 | 2.550938e+04 | 8.718396e+03 | 2.276470e+04 | 5.553941e+04 | 6.780975e+03 | ... | 0.000000e+00 | 2.599374e+04 | 1.146308e+04 | 8.072589e+02 | 4.903290e+05 | 2.437922e+04 | 1.438535e+05 | 2.437922e+04 | 2.502503e+04 | 2.034292e+04 |
5 rows × 26 columns
In [ ]:
title = "P8 FEEDING Genotype WT vs Homo vs HET"
P8_F_color_dict_gt_h_h,P8_F_row_colors_gt_h_h = genotypecolordict(P8_F_metadata_gt_h_h,"Genotype")
plot_cluster_heatmap(P8_F_h_h_data_heatmap,P8_F_color_dict_gt_h_h,P8_F_row_colors_gt_h_h,title)
P8 Feeding WT HOMOHET¶
In [ ]:
# Load data
P8_F_gt_hh = pd.read_csv('Different_Time_Points/P8/FEEDING_WT_HOMOHET/TSV/data_normalized.txt',sep='\t',index_col=0)
P8_F_gt_metadata_hh = pd.read_csv('Different_Time_Points/P8/FEEDING_WT_HOMOHET/TSV/P8_F_Het+Homo.txt',sep='\t',index_col=0)
P8_F_gt_hh.head(5)
Out[Â ]:
| 4 | 5 | 6 | 9 | 10 | 16 | 17 | 18 | 19 | 20 | ... | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 44 | 45 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Lactobacillus | 2.757596e+05 | 7.052214e+05 | 2.520262e+05 | 1.469857e+06 | 5.145468e+05 | 4.352740e+05 | 1.942265e+05 | 1.465013e+06 | 5.038910e+05 | 3.233556e+06 | ... | 2.221576e+06 | 5.631438e+05 | 8.363202e+04 | 4.975136e+06 | 5.505667e+06 | 4.188059e+05 | 7.666538e+06 | 7.145210e+06 | 3.528206e+06 | 7.570474e+05 |
| Escherichia_Shigella | 8.785398e+06 | 7.533017e+06 | 7.648132e+06 | 7.174271e+06 | 7.895476e+06 | 9.028545e+06 | 8.464594e+06 | 8.180116e+06 | 8.602958e+06 | 5.924634e+06 | ... | 7.336046e+06 | 8.683038e+06 | 8.799445e+06 | 2.421777e+04 | 1.291614e+04 | 1.002616e+05 | 1.501502e+04 | 1.727534e+04 | 5.471116e+06 | 8.344796e+06 |
| Muribacter | 7.265330e+03 | 2.723691e+05 | 8.347057e+04 | 5.392489e+04 | 2.216733e+05 | 1.372340e+04 | 1.291614e+04 | 1.227033e+04 | 3.180600e+04 | 5.973716e+03 | ... | 1.517647e+04 | 4.843553e+03 | 2.906132e+04 | 2.269528e+06 | 9.530498e+05 | 5.574607e+06 | 4.084730e+04 | 6.120637e+05 | 1.356195e+04 | 1.888986e+04 |
| Staphylococcus | 1.414318e+05 | 8.761988e+05 | 7.265330e+04 | 7.593077e+05 | 1.004069e+06 | 8.411637e+04 | 6.813265e+04 | 1.248022e+05 | 1.906745e+05 | 1.018761e+05 | ... | 1.937421e+03 | 5.005005e+03 | 0.000000e+00 | 3.137008e+05 | 1.653751e+06 | 1.076883e+05 | 7.234654e+05 | 1.888986e+05 | 2.599374e+04 | 1.243179e+04 |
| Streptococcus | 6.780975e+03 | 5.553941e+04 | 2.502503e+04 | 2.437922e+04 | 8.944428e+04 | 1.033291e+04 | 2.437922e+04 | 2.550938e+04 | 2.599374e+04 | 2.276470e+04 | ... | 8.718396e+03 | 1.146308e+04 | 8.072589e+02 | 4.903290e+05 | 6.445155e+05 | 4.294617e+04 | 1.438535e+05 | 4.062127e+05 | 1.855081e+05 | 7.555943e+04 |
5 rows × 26 columns
In [ ]:
P8_F_gt_metadata_hh = format_meta(P8_F_gt_metadata_hh,"Genotype")
P8_F_gt_metadata_hh.head(5)
Out[Â ]:
| Experiment | Tissue type | DOL | Genotype | |
|---|---|---|---|---|
| 4 | F | colon | DOL8 | Sig Homo_HET |
| 5 | F | colon | DOL8 | Sig WT |
| 6 | F | colon | DOL8 | Sig Homo_HET |
| 9 | F | colon | DOL8 | Sig Homo_HET |
| 10 | F | colon | DOL8 | Sig WT |
In [ ]:
P8_F_gt_metadata_hh= sort_meta_GT(P8_F_gt_metadata_hh,"Genotype")
P8_F_gt_metadata_hh.head(5)
Out[Â ]:
| Experiment | Tissue type | DOL | Genotype | |
|---|---|---|---|---|
| 4 | F | colon | DOL8 | Sig Homo_HET |
| 5 | F | colon | DOL8 | Sig WT |
| 6 | F | colon | DOL8 | Sig Homo_HET |
| 9 | F | colon | DOL8 | Sig Homo_HET |
| 10 | F | colon | DOL8 | Sig WT |
In [ ]:
P8_F_hh_data_heatmap = create_heatmap(P8_F_gt_hh,P8_F_gt_metadata_hh,"Genotype")
P8_F_hh_data_heatmap.head(5)
Out[Â ]:
| 32 | 44 | 39 | 10 | 18 | 36 | 20 | 27 | 5 | 4 | ... | 31 | 29 | 26 | 21 | 19 | 17 | 16 | 9 | 6 | 45 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Lactobacillus | 2.221576e+06 | 3.528206e+06 | 7.145210e+06 | 5.145468e+05 | 1.465013e+06 | 5.505667e+06 | 3.233556e+06 | 3.677871e+05 | 7.052214e+05 | 2.757596e+05 | ... | 4.552940e+04 | 2.356066e+06 | 9.685492e+05 | 1.649714e+06 | 5.038910e+05 | 1.942265e+05 | 4.352740e+05 | 1.469857e+06 | 2.520262e+05 | 7.570474e+05 |
| Escherichia_Shigella | 7.336046e+06 | 5.471116e+06 | 1.727534e+04 | 7.895476e+06 | 8.180116e+06 | 1.291614e+04 | 5.924634e+06 | 8.919403e+06 | 7.533017e+06 | 8.785398e+06 | ... | 9.122510e+06 | 7.086280e+06 | 7.507508e+06 | 7.093868e+06 | 8.602958e+06 | 8.464594e+06 | 9.028545e+06 | 7.174271e+06 | 7.648132e+06 | 8.344796e+06 |
| Muribacter | 1.517647e+04 | 1.356195e+04 | 6.120637e+05 | 2.216733e+05 | 1.227033e+04 | 9.530498e+05 | 5.973716e+03 | 1.905131e+04 | 2.723691e+05 | 7.265330e+03 | ... | 5.327909e+03 | 5.489360e+03 | 1.130162e+03 | 4.020149e+04 | 3.180600e+04 | 1.291614e+04 | 1.372340e+04 | 5.392489e+04 | 8.347057e+04 | 1.888986e+04 |
| Staphylococcus | 1.937421e+03 | 2.599374e+04 | 1.888986e+05 | 1.004069e+06 | 1.248022e+05 | 1.653751e+06 | 1.018761e+05 | 1.650037e+05 | 8.761988e+05 | 1.414318e+05 | ... | 1.775970e+03 | 1.630663e+05 | 1.243179e+04 | 2.986858e+04 | 1.906745e+05 | 6.813265e+04 | 8.411637e+04 | 7.593077e+05 | 7.265330e+04 | 1.243179e+04 |
| Streptococcus | 8.718396e+03 | 1.855081e+05 | 4.062127e+05 | 8.944428e+04 | 2.550938e+04 | 6.445155e+05 | 2.276470e+04 | 1.727534e+04 | 5.553941e+04 | 6.780975e+03 | ... | 0.000000e+00 | 4.004004e+04 | 1.291614e+03 | 5.327909e+03 | 2.599374e+04 | 2.437922e+04 | 1.033291e+04 | 2.437922e+04 | 2.502503e+04 | 7.555943e+04 |
5 rows × 26 columns
In [ ]:
title = "P8 FEEDING Genotype WT vs Homo_Het"
P8_F_color_dict_gt_hh,P8_F_row_colors_gt_hh = genotypecolordict(P8_F_gt_metadata_hh,"Genotype")
# print(P8_F_row_colors_gt_hh)
plot_cluster_heatmap(P8_F_hh_data_heatmap,P8_F_color_dict_gt_hh,P8_F_row_colors_gt_hh,title)
P11 COLON WT HOMO HET CONTROL¶
In [ ]:
# Load data
P11_c_gt_h_h = pd.read_csv('Different_Time_Points/P11_COLON_WT_Homo_HET/CONTROL/TSV/data_normalized.txt',sep='\t',index_col=0)
P11_c_gt_metadata_h_h = pd.read_csv('Different_Time_Points/P11_COLON_WT_Homo_HET/CONTROL/TSV/P11_COLON_WT_Homo_HET_C.txt',sep='\t',index_col=0)
P11_c_gt_metadata_h_h = format_meta(P11_c_gt_metadata_h_h,"Genotype")
P11_C_h_h_data_heatmap = create_heatmap(P11_c_gt_h_h,P11_c_gt_metadata_h_h,"Genotype")
title = "P11 COLON Genotype WT vs Homo vs Het CONTROL"
P11_C_color_dict_gt_h_h,P11_C_row_colors_gt_h_h = genotypecolordict(P11_c_gt_metadata_h_h,"Genotype")
plot_cluster_heatmap(P11_C_h_h_data_heatmap,P11_C_color_dict_gt_h_h,P11_C_row_colors_gt_h_h,title)
In [ ]:
# Load data
P11_NEC_gt_h_h = pd.read_csv('Different_Time_Points/P11_COLON_WT_Homo_HET/NEC/TSV/data_normalized.txt',sep='\t',index_col=0)
P11_NEC_gt_metadata_h_h = pd.read_csv('Different_Time_Points/P11_COLON_WT_Homo_HET/NEC/TSV/P11_COLON_WT_Homo_HET_NEC.txt',sep='\t',index_col=0)
P11_NEC_gt_metadata_h_h = format_meta(P11_NEC_gt_metadata_h_h,"Genotype")
P11_NEC_h_h_data_heatmap = create_heatmap(P11_NEC_gt_h_h,P11_NEC_gt_metadata_h_h,"Genotype")
title = "P11 COLON Genotype WT vs Homo vs Het NEC"
P11_NEC_color_dict_gt_h_h,P11_NEC_row_colors_gt_h_h = genotypecolordict(P11_NEC_gt_metadata_h_h,"Genotype")
plot_cluster_heatmap(P11_NEC_h_h_data_heatmap,P11_NEC_color_dict_gt_h_h,P11_NEC_row_colors_gt_h_h,title)
In [ ]:
# Load data
P11_TI_C_gt_h_h = pd.read_csv('Different_Time_Points/P11_Ileal_WT_Homo_HET/CONTROL/tsv/data_normalized.txt',sep='\t',index_col=0)
P11_TI_C_gt_metadata_h_h = pd.read_csv('Different_Time_Points/P11_Ileal_WT_Homo_HET/CONTROL/tsv/P11_Ileal_WT_Homo_HET_C.txt',sep='\t',index_col=0)
P11_TI_C_gt_metadata_h_h = format_meta(P11_TI_C_gt_metadata_h_h,"Genotype")
P11_TI_C_h_h_data_heatmap = create_heatmap(P11_TI_C_gt_h_h,P11_TI_C_gt_metadata_h_h,"Genotype")
title = "P11 TI Genotype WT vs Homo vs Het Control"
P11_TI_C_color_dict_gt_h_h,P11_TI_C_row_colors_gt_h_h = genotypecolordict(P11_TI_C_gt_metadata_h_h,"Genotype")
plot_cluster_heatmap(P11_TI_C_h_h_data_heatmap,P11_TI_C_color_dict_gt_h_h,P11_TI_C_row_colors_gt_h_h,title)
In [ ]:
# Load data
P11_TI_NEC_gt_h_h = pd.read_csv('Different_Time_Points/P11_Ileal_WT_Homo_HET/NEC/tsv/data_normalized.txt',sep='\t',index_col=0)
P11_TI_NEC_gt_metadata_h_h = pd.read_csv('Different_Time_Points/P11_Ileal_WT_Homo_HET/NEC/tsv/P11_Ileal_WT_Homo_HET_NEC.txt',sep='\t',index_col=0)
P11_TI_NEC_gt_metadata_h_h = format_meta(P11_TI_NEC_gt_metadata_h_h,"Genotype")
P11_TI_NEC_h_h_data_heatmap = create_heatmap(P11_TI_NEC_gt_h_h,P11_TI_NEC_gt_metadata_h_h,"Genotype")
title = "P11 TI Genotype WT vs Homo vs Het NEC"
P11_TI_NEC_color_dict_gt_h_h,P11_TI_NEC_row_colors_gt_h_h = genotypecolordict(P11_TI_NEC_gt_metadata_h_h,"Genotype")
plot_cluster_heatmap(P11_TI_NEC_h_h_data_heatmap,P11_TI_NEC_color_dict_gt_h_h,P11_TI_NEC_row_colors_gt_h_h,title)
In [ ]:
# Load data
P11_TI_C_gt_hh = pd.read_csv('Different_Time_Points/P11_Ileal_WT_HomoHET/CONTROL/tsv/data_normalized.txt',sep='\t',index_col=0)
P11_TI_C_gt_metadata_hh = pd.read_csv('Different_Time_Points/P11_Ileal_WT_HomoHET/CONTROL/tsv/P11_Ileal_WT_HomoHET_C.txt',sep='\t',index_col=0)
P11_TI_C_gt_metadata_hh = format_meta(P11_TI_C_gt_metadata_hh,"Genotype")
P11_TI_C_hh_data_heatmap = create_heatmap(P11_TI_C_gt_hh,P11_TI_C_gt_metadata_hh,"Genotype")
title = "P11 TI Genotype WT vs Homo_Het CONTROL"
P11_TI_C_color_dict_gt_hh,P11_TI_C_row_colors_gt_hh = genotypecolordict(P11_TI_C_gt_metadata_hh,"Genotype")
plot_cluster_heatmap(P11_TI_C_hh_data_heatmap,P11_TI_C_color_dict_gt_hh,P11_TI_C_row_colors_gt_hh,title)
In [ ]:
# Load data
P11_TI_NEC_gt_hh = pd.read_csv('Different_Time_Points/P11_Ileal_WT_HomoHET/NEC/tsv/data_normalized.txt',sep='\t',index_col=0)
P11_TI_NEC_gt_metadata_hh = pd.read_csv('Different_Time_Points/P11_Ileal_WT_HomoHET/NEC/tsv/P11_Ileal_WT_HomoHET_NEC.txt',sep='\t',index_col=0)
P11_TI_NEC_gt_metadata_hh = format_meta(P11_TI_NEC_gt_metadata_hh,"Genotype")
P11_TI_NEC_hh_data_heatmap = create_heatmap(P11_TI_NEC_gt_hh,P11_TI_NEC_gt_metadata_hh,"Genotype")
title = "P11 TI Genotype WT vs Homo_Het NEC"
P11_TI_NEC_color_dict_gt_hh,P11_TI_NEC_row_colors_gt_hh = genotypecolordict(P11_TI_NEC_gt_metadata_hh,"Genotype")
plot_cluster_heatmap(P11_TI_NEC_hh_data_heatmap,P11_TI_NEC_color_dict_gt_hh,P11_TI_NEC_row_colors_gt_hh,title)
In [ ]:
# Load data
P14_COLON_C_gt_h_h = pd.read_csv('Different_Time_Points/P14_COLON_WT_Homo_HET/CONTROL/tsv/data_normalized.txt',sep='\t',index_col=0)
P14_COLON_C_gt_metadata_h_h = pd.read_csv('Different_Time_Points/P14_COLON_WT_Homo_HET/CONTROL/tsv/P14_COLON_WT_Homo_HET_C.txt',sep='\t',index_col=0)
P14_COLON_C_gt_metadata_h_h = format_meta(P14_COLON_C_gt_metadata_h_h,"Genotype")
P14_COLON_C_h_h_data_heatmap = create_heatmap(P14_COLON_C_gt_h_h,P14_COLON_C_gt_metadata_h_h,"Genotype")
title = "P14 COLON Genotype WT vs Homo vs Het Control"
P14_COLON_C_color_dict_gt_h_h,P14_COLON_C_row_colors_gt_h_h = genotypecolordict(P14_COLON_C_gt_metadata_h_h,"Genotype")
plot_cluster_heatmap(P14_COLON_C_h_h_data_heatmap,P14_COLON_C_color_dict_gt_h_h,P14_COLON_C_row_colors_gt_h_h,title)
In [ ]:
# Load data
P14_COLON_C_gt_hh = pd.read_csv('Different_Time_Points/P14_COLON_WT_HomoHET/tsv/data_normalized.txt',sep='\t',index_col=0)
P14_COLON_C_gt_metadata_hh = pd.read_csv('Different_Time_Points/P14_COLON_WT_HomoHET/tsv/P14_COLON_WT_HomoHET.txt',sep='\t',index_col=0)
P14_COLON_C_gt_metadata_hh = format_meta(P14_COLON_C_gt_metadata_hh,"Genotype")
P14_COLON_C_hh_data_heatmap = create_heatmap(P14_COLON_C_gt_hh,P14_COLON_C_gt_metadata_hh,"Genotype")
title = "P14 COLON Genotype WT vs Homo_Het Control"
P14_COLON_C_color_dict_gt_hh,P14_COLON_C_row_colors_gt_hh = genotypecolordict(P14_COLON_C_gt_metadata_hh,"Genotype")
plot_cluster_heatmap(P14_COLON_C_hh_data_heatmap,P14_COLON_C_color_dict_gt_hh,P14_COLON_C_row_colors_gt_hh,title)
Take a look¶
In [ ]:
# Load data
P14_TI_C_gt_h_h = pd.read_csv('Different_Time_Points/P14_Ileal_WT_Homo_HET/tsv/data_normalized.txt',sep='\t',index_col=0)
P14_TI_C_gt_metadata_h_h = pd.read_csv('Different_Time_Points/P14_Ileal_WT_Homo_HET/tsv/P14_Ileal_WT_Homo_HET_C.txt',sep='\t',index_col=0)
P14_TI_C_gt_metadata_h_h = format_meta(P14_TI_C_gt_metadata_h_h,"Genotype")
P14_TI_C_h_h_data_heatmap = create_heatmap(P14_TI_C_gt_h_h,P14_TI_C_gt_metadata_h_h,"Genotype")
title = "P14 TI Genotype WT vs Homo vs Het Control"
P14_TI_C_color_dict_gt_h_h,P14_TI_C_row_colors_gt_h_h = genotypecolordict(P14_TI_C_gt_metadata_h_h,"Genotype")
plot_cluster_heatmap(P14_TI_C_h_h_data_heatmap,P14_TI_C_color_dict_gt_h_h,P14_TI_C_row_colors_gt_h_h,title)
In [ ]:
# Load data
P14_TI_C_gt_hh = pd.read_csv('Different_Time_Points/P14_Ileal_WT_HomoHET/tsv/data_normalized.txt',sep='\t',index_col=0)
P14_TI_C_gt_metadata_hh = pd.read_csv('Different_Time_Points/P14_Ileal_WT_HomoHET/tsv/P14_Ileal_WT_HomoHET.txt',sep='\t',index_col=0)
P14_TI_C_gt_metadata_hh = format_meta(P14_TI_C_gt_metadata_hh,"Genotype")
P14_TI_C_hh_data_heatmap = create_heatmap(P14_TI_C_gt_hh,P14_TI_C_gt_metadata_hh,"Genotype")
title = "P14 TI Genotype WT vs Homo_Het Control"
P14_TI_C_color_dict_gt_hh,P14_TI_C_row_colors_gt_hh = genotypecolordict(P14_TI_C_gt_metadata_hh,"Genotype")
plot_cluster_heatmap(P14_TI_C_hh_data_heatmap,P14_TI_C_color_dict_gt_hh,P14_TI_C_row_colors_gt_hh,title)
In [ ]:
# Load data
P28_COLON_C_gt_h_h = pd.read_csv('Different_Time_Points/P28_COLON_WT_Homo_HET/tsv/data_normalized.txt',sep='\t',index_col=0)
P28_COLON_C_gt_metadata_h_h = pd.read_csv('Different_Time_Points/P28_COLON_WT_Homo_HET/tsv/P28_COLON_WT_Homo_HET.txt',sep='\t',index_col=0)
P28_COLON_C_gt_metadata_h_h = format_meta(P28_COLON_C_gt_metadata_h_h,"Genotype")
P28_COLON_C_h_h_data_heatmap = create_heatmap(P28_COLON_C_gt_h_h,P28_COLON_C_gt_metadata_h_h,"Genotype")
title = "P28 COLON Genotype WT vs Homo vs Het Control"
P28_COLON_C_color_dict_gt_h_h,P28_COLON_C_row_colors_gt_h_h = genotypecolordict(P28_COLON_C_gt_metadata_h_h,"Genotype")
plot_cluster_heatmap(P28_COLON_C_h_h_data_heatmap,P28_COLON_C_color_dict_gt_h_h,P28_COLON_C_row_colors_gt_h_h,title)
In [ ]:
# Load data
P28_COLON_C_gt_hh = pd.read_csv('Different_Time_Points/P28_COLON_WT_HomoHET/tsv/data_normalized.txt',sep='\t',index_col=0)
P28_COLON_C_gt_metadata_hh = pd.read_csv('Different_Time_Points/P28_COLON_WT_HomoHET/tsv/P28_COLON_WT_HomoHET.txt',sep='\t',index_col=0)
P28_COLON_C_gt_metadata_hh = format_meta(P28_COLON_C_gt_metadata_hh,"Genotype")
P28_COLON_C_hh_data_heatmap = create_heatmap(P28_COLON_C_gt_hh,P28_COLON_C_gt_metadata_hh,"Genotype")
title = "P28 COLON Genotype WT vs Homo_Het Control"
P28_COLON_C_color_dict_gt_hh,P28_COLON_C_row_colors_gt_hh = genotypecolordict(P28_COLON_C_gt_metadata_hh,"Genotype")
plot_cluster_heatmap(P28_COLON_C_hh_data_heatmap,P28_COLON_C_color_dict_gt_hh,P28_COLON_C_row_colors_gt_hh,title)
In [ ]:
# Load data
P28_TI_C_gt_h_h = pd.read_csv('Different_Time_Points/P28_Ileal_WT_Homo_HET/tsv/data_normalized.txt',sep='\t',index_col=0)
P28_TI_C_gt_metadata_h_h = pd.read_csv('Different_Time_Points/P28_Ileal_WT_Homo_HET/tsv/P28_Ileal_WT_Homo_HET.txt',sep='\t',index_col=0)
P28_TI_C_gt_metadata_h_h = format_meta(P28_TI_C_gt_metadata_h_h,"Genotype")
P28_TI_C_h_h_data_heatmap = create_heatmap(P28_TI_C_gt_h_h,P28_TI_C_gt_metadata_h_h,"Genotype")
title = "P28 TI Genotype WT vs Homo vs Het Control"
P28_TI_C_color_dict_gt_h_h,P28_TI_C_row_colors_gt_h_h = genotypecolordict(P28_TI_C_gt_metadata_h_h,"Genotype")
plot_cluster_heatmap(P28_TI_C_h_h_data_heatmap,P28_TI_C_color_dict_gt_h_h,P28_TI_C_row_colors_gt_h_h,title)
In [ ]:
# Load data
P28_TI_C_gt_hh = pd.read_csv('Different_Time_Points/P28_Ileal_WT_HomoHET/tsv/data_normalized.txt',sep='\t',index_col=0)
P28_TI_C_gt_metadata_hh = pd.read_csv('Different_Time_Points/P28_Ileal_WT_HomoHET/tsv/P11_P14_P28_Ileal_WT_HomoHET.txt',sep='\t',index_col=0)
P28_TI_C_gt_metadata_hh = format_meta(P28_TI_C_gt_metadata_hh,"Genotype")
P28_TI_C_hh_data_heatmap = create_heatmap(P28_TI_C_gt_hh,P28_TI_C_gt_metadata_hh,"Genotype")
title = "P28 TI Genotype WT vs Homo_Het Control"
P28_TI_C_color_dict_gt_hh,P28_TI_C_row_colors_gt_hh = genotypecolordict(P28_TI_C_gt_metadata_hh,"Genotype")
plot_cluster_heatmap(P28_TI_C_hh_data_heatmap,P28_TI_C_color_dict_gt_hh,P28_TI_C_row_colors_gt_hh,title)
In [ ]:
# Load data
P11_c_gt_hh = pd.read_csv('Different_Time_Points/P11_COLON_WT_HomoHET/CONTROL/TSV/data_normalized.txt',sep='\t',index_col=0)
P11_c_gt_metadata_hh = pd.read_csv('Different_Time_Points/P11_COLON_WT_HomoHET/CONTROL/TSV/P11_COLON_WT_HomoHET_C.txt',sep='\t',index_col=0)
P11_c_gt_metadata_hh = format_meta(P11_c_gt_metadata_hh,"Genotype")
P11_C_hh_data_heatmap = create_heatmap(P11_c_gt_hh,P11_c_gt_metadata_hh,"Genotype")
title = "P11 COLON Genotype WT vs Homo_Het CONTROL"
P11_C_color_dict_gt_hh,P11_C_row_colors_gt_hh = genotypecolordict(P11_c_gt_metadata_hh,"Genotype")
plot_cluster_heatmap(P11_C_hh_data_heatmap,P11_C_color_dict_gt_hh,P11_C_row_colors_gt_hh,title)
In [ ]:
# Load data
P11_NEC_gt_hh = pd.read_csv('Different_Time_Points/P11_COLON_WT_HomoHET/NEC/TSV/data_normalized.txt',sep='\t',index_col=0)
P11_NEC_gt_metadata_hh = pd.read_csv('Different_Time_Points/P11_COLON_WT_HomoHET/NEC/TSV/P11_COLON_WT_HomoHET_NEC.txt',sep='\t',index_col=0)
P11_NEC_gt_metadata_hh = format_meta(P11_NEC_gt_metadata_hh,"Genotype")
P11_NEC_hh_data_heatmap = create_heatmap(P11_NEC_gt_hh,P11_NEC_gt_metadata_hh,"Genotype")
title = "P11 COLON Genotype WT vs Homo_Het NEC"
P11_NEC_color_dict_gt_hh,P11_NEC_row_colors_gt_hh = genotypecolordict(P11_NEC_gt_metadata_hh,"Genotype")
plot_cluster_heatmap(P11_NEC_hh_data_heatmap,P11_NEC_color_dict_gt_hh,P11_NEC_row_colors_gt_hh,title)
